This pilot study will build an agent-based model that will describe heroin use and recovery trajectories in the context of complex interconnections with current treatment practices, recovery-oriented services, and the illicit drug market. Treatment of heroin addiction is associated with a chronic cycle of relapse, treatment reentry, and recovery, often lasting for decades. The most commonly used treatment for heroin addiction is methadone therapy, which is pharmacologically efficient, but due to a complex interaction of organizational, community, and policy factors that affect relapse to heroin use, it is not always optimally effective and is thus unable to prevent relapse in many addicts. To date, a number of studies have collected information about heroin addiction recovery trajectories; however no model has yet been developed to integrate influential factors into one model, considering them as part of a system. The proposed effort represents the first systems modeling approach to address the topic of heroin recovery, including influences from contextual factors. Accordingly, the development of the proposed model simultaneously takes into account characteristics of heroin addiction treatment strategies (e.g., residential vs. outpatient modality) and the user's contextual environment (e.g., social networks, illicit drug markets) to more aptly assess the processes that promote or, conversely, interfere with recovery. Model parameters will be obtained from well studied datasets on heroin use trajectories identified by leading experts from UCLA and Chestnut Health Systems, as well as from other relevant studies. The resulting model will be used to address questions about the optimal combination and staging of treatment approaches and to explore whether some combinations could lead to qualitative (e.g., cessation) rather than simply quantitative (e.g., delayed relapse) changes in recovery cycle. Specifically, we aim to (1) Develop an agent-based model of heroin use and recovery process that would describe the main systems components influencing the success of recovery, (2) Through simulated experiments, evaluate the success of specific complex strategies aimed to increase treatment effectiveness, and (3) Evaluate the feasibility of approaches that show the most promise, address potential resistance to policy strategies, and evaluate generalizability of the model in regard to other drug treatments.